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PROJECT TOPIC: DETERMINE STRING CLUSTERING USING SELF-ORGANIZING MAP ALGORITHM
Department: Computer Science
FORMART: MS WORD
PAGES: 82 pages, abstract, chapter 1-5 , APENDIX A source code and APENDIX B output, well reserached and supervised82 pages, abstract, chapter 1-5 , APENDIX A source code and APENDIX B output, well reserached and supervised
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In a number of real-life applications, the user is interested in analysing several sources of information together: a graph combined with the additional information known on its nodes, numerical variables measured on individuals and factors describing these individuals.
The self-organizing map (SOM) is an automatic data-analysis method. It is widely applied to clustering problems and data exploration in industry, finance, natural sciences, and linguistics. The most extensive applications, exemplified in this work, can be found in the management of massive textual databases and in bioinformatics. With the rapid development of information management system, retrieving relevant information is difficult task. The efficient clustering algorithms are needed to improve the results of the retrieval. Document clustering is a process of recognizing the similarity or dissimilarity among the given objects and forms subgroups sharing common characteristics. This research focuses on string clustering of staff records in the graduate school using self organising map algorithm. The proposed system was designed and implemented using object oriented programming language java and my MYSQL database.